Results 11 to 20 of about 741,734 (189)
Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python
Journal of Statistical Software, 2022 The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications. This is the result of a variety of methodological advances with faster and cheaper hardware as well as the ...
Tomás Capretto +5 more
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Frontiers in Psychology, 2017 We investigated the effects of violations of the sphericity assumption on Type I error rates for different methodical approaches of repeated measures analysis using a simulation approach.
Nicolas Haverkamp, André Beauducel
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Increased interpretation of deep learning models using hierarchical cluster-based modelling.
PLoS ONE, 2023 Linear prediction models based on data with large inhomogeneity or abrupt non-linearities often perform poorly because relationships between groups in the data dominate the model.
Elise Lunde Gjelsvik, Kristin Tøndel
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Posterior propriety of an objective prior for generalized hierarchical normal linear models
Statistical Theory and Related Fields, 2022 Bayesian Hierarchical models has been widely used in modern statistical application. To deal with the data having complex structures, we propose a generalized hierarchical normal linear (GHNL) model which accommodates arbitrarily many levels, usual ...
Cong Lin, Dongchu Sun, Chengyuan Song
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Examining Non-cognitive Factors Predicting Reading Achievement in Turkey: Evidence from PISA 2018
International Journal of Contemporary Educational Research, 2022 The purpose of the study was to investigate how student and teacher-related non-cognitive variables were important factors on the reading performances of Turkish students in PISA 2018.
Pınar KARAMAN
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The supervised hierarchical Dirichlet process [PDF]
, 2014 We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group.
Dai, Andrew M., Storkey, Amos J.
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Modeling Human Morphological Competence
Frontiers in Psychology, 2020 One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings ...
Yohei Oseki +4 more
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Journal of Research in Education Sciences, 2010 本研究目的在瞭解參與集中式資優班的國中資優生，其科學自我概念是否存在負向「大魚小池效應」（big fish little pond effect），抑或資優班的標籤反而帶給個人正向的榮耀感效應（reflected glory effects）？研究樣本包含367 位資優生及1,364位一般生，以科學自我概念量表、自然科學業成就測驗與班級榮耀感量表為工具，使用HLM 軟體進行學生及班級二階層模式（multilevel modeling）的統計分析。研究結論為：一、我國資優班學生的科學自我概念存在明顯的負向「
侯雅齡 Ya-Ling Hou
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HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R
Journal of Statistical Software, 2014 Over the last twenty years there have been numerous developments in diagnostic pro- cedures for hierarchical linear models; however, these procedures are not widely imple- mented in statistical software packages, and those packages that do contain a ...
Adam Loy, Heike Hofmann
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Deep Gaussian Mixture Models [PDF]
, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed.
McLachlan, Geoffrey J., Viroli, Cinzia
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